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19 characterizing pv modules using microinverter data final

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8th PVPMC Workshop, May 9-10 2017

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19 characterizing pv modules using microinverter data final

  1. 1. Characterizing PV modules using Microinverter data Nathan Charles
  2. 2. | © 2017 Enphase Energy, Inc.2 The Enphase Home Energy Solution with IQ Enphase Battery Enphase Envoy Enphase Installer Toolkit Enphase Enlighten Enphase IQ 6 / IQ 6+ Micro Enphase Q Cable
  3. 3. | © 2017 Enphase Energy, Inc.3 • Enlighten displays detailed data down to module level • Enphase has produced ~15 million microinverters. Every inverter reports time stamped sensor data including: • Internal temperature • DC voltage • DC current • Over 500,000 systems in Enlighten with ~ 4 million inverter years of data Microinverter data is reported to Enlighten
  4. 4. | © 2017 Enphase Energy, Inc.4 • Weather adjusted PR is commonly used for Commercial and Utility Scale PV systems but requires: • Instrumentation of module temperature • Local POA Irradiance source, maintained and calibrated over time for accuracy • Accurate tilt and azimuth of test unit(s), and data transposition for modules slightly differently oriented within the same array or system • This isn’t practical for small residential systems because: • There is typically no local instrumentation • As-built may not match design; and • The time required to analyze a system is prohibitive Problem: How to flag asset underperformance?
  5. 5. | © 2017 Enphase Energy, Inc.5 Infer Site/Orientation Metadata • Module Tilt • Module Azimuth • Module Latitude • Module Longitude Infer PV Module Metadata • Voltage reference (Vmp) • Current reference (Imp) • Temperature coefficient Signal analysis of system behavior • Series loss step changes • Shunt loss trends Approach and process for data analysis • Power != Energy - Micros report instantaneous values. DC current and DC voltage values gives us significantly greater insight into environmental issues than just energy, but require different methods of analysis. • Bottom-up, start at the individual inverter level and work up to system and fleet level • Analysis should not be dependent on external sensors, data sources, or meta data • Simple is better than complicated – the dataset is too large for expensive processing • Process should run automatically without intervention
  6. 6. | © 2017 Enphase Energy, Inc.6 1. Filter historical data for 24 hour interval with most Ah per month. This should be correlated with clear sky irradiance. 2. With Kendall correlation coefficient between clear sky irradiance (W/m^2) and dc current (A) as a cost function; 3. maximize correlation of current and clear sky irradiance by adjusting tilt and azimuth. • This process tests model and installer entered meta data. • Methodology performance tested on a population in a small fleet (n=190) Tilt & Azimuth inference methodology Infer Site/Orientation Metadata
  7. 7. | © 2017 Enphase Energy, Inc.7 Azimuth analysis • Azimuth is strongly correlated with relatively few outliers. RMSE 14.4° with 5.8° bias west of entered data. • Why do we have outliers; model error or incorrect meta data? Infer Site/Orientation Metadata
  8. 8. | © 2017 Enphase Energy, Inc.8 Tilt analysis • RMSE 5.7° but only a 1.4° degree mean bias inferred higher than entered. • Installers may be making assumptions e.g. 25° or 18° instead of taking a measurement • Roof tilt is probably not a standard distribution. Infer Site/Orientation Metadata
  9. 9. | © 2017 Enphase Energy, Inc.9 Example 1 • Installer entered: 180° • Regression: 259° • Perhaps install crew confused job info? Infer Site/Orientation Metadata
  10. 10. | © 2017 Enphase Energy, Inc.10 Example 2 • Installer entered: 164° • Regression: 140° • Google maps shows 142 ° • Was the measurement source calibrated? Infer Site/Orientation Metadata
  11. 11. | © 2017 Enphase Energy, Inc.11 Example 3 • Installer entered: 105° • Regression didn’t lead to meaningful data. • Google maps shows a system where dormers would shade all substrings of PV module. A period where only one diode would latch is unlikely. Infer Site/Orientation Metadata
  12. 12. | © 2017 Enphase Energy, Inc.12 Example 4 • Installer entered: 116° • Regression: 210° • Array view looks correct, however meta data in Enlighten is transposed. 8 modules should be at 206° instead of 116° Infer Site/Orientation Metadata
  13. 13. | © 2017 Enphase Energy, Inc.13 Regression model used to model cell temperature from micro temperature accounts for: • Thermal latency of between sensor and cell • Heating from efficiency losses • Temperature delta between cell temperature and microinverter Cell temperature Infer Module Metadata
  14. 14. | © 2017 Enphase Energy, Inc.14 Module Metadata: Voltage & Temperature Slope of this line is should be correlated to module temperature coefficient. Value at 25 degrees C should be correlated with Vmp Infer Module Metadata
  15. 15. | © 2017 Enphase Energy, Inc.15 Module Metadata: DC Current DC Current vs Clearsky Irradiance RANSAC, a robust fit shallow machine learning algorithm, used to find slope of the ridge. 1000 W/m^2 value should be similar to Imp Infer Module Metadata
  16. 16. | © 2017 Enphase Energy, Inc.16 Irradiance Performance Ratio over time Clear sky vs Actual over time. Step up in high current steady state is likely a rain event. Signal analysis of system behavior
  17. 17. | © 2017 Enphase Energy, Inc.17 Temperature compensated voltage over time PCU Temperature used to adjust voltage Voltage step down is attributed to module sub-string issue Signal analysis of system behavior
  18. 18. | © 2017 Enphase Energy, Inc.18 • Correlation maximization leads to meaningful inference of azimuth (7° median deviation) • Significant deviations between inferred and reported azimuth are typically representational of incorrect meta data or sub-optimal design • Inferred PV module characteristics allow visualization of module performance over time Microinverter data improves understanding of fleet performance Infer Site/Orientation Metadata • Module Tilt • Module Azimuth • Module Latitude • Module Longitude Infer PV Module Metadata • Voltage reference (Vmp) • Current reference (Imp) • Temperature coefficient Signal analysis of system behavior • Series loss step changes • Shunt loss trends
  19. 19. Thank you Questions?

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